multilevel.fit: Simultaneous and Level-Specific Multilevel Model Fit...

In misty: Miscellaneous Functions 'T. Yanagida'

Simultaneous and Level-Specific Multilevel Model Fit Information

Description

This function provides simultaneous and level-specific model fit information using the partially saturated model method for multilevel models estimated with the lavaan package. Note that level-specific fit indices cannot be computed when the fitted model contains cross-level constraints, e.g., equal factor loadings across levels in line with the metric cross-level measurement invariance assumption.

Usage

multilevel.fit(x, print = c("all", "summary", "fit"), digits = 3, p.digits = 3,
               write = NULL, append = TRUE, check = TRUE, output = TRUE)

Arguments

x	a fitted model of class "lavaan" from the lavaan package.
print	a character string or character vector indicating which results to show on the console, i.e. "all" for all results, "summary" for a summary of the specification of the estimation method and missing data handling in lavaan and "fit" for model fit.
digits	an integer value indicating the number of decimal places to be used for displaying results. Note that loglikelihood, information criteria and chi-square test statistic is printed with digits minus 1 decimal places.
p.digits	an integer value indicating the number of decimal places to be used for displaying the p-value.
write	a character string naming a file for writing the output into either a text file with file extension ".txt" (e.g., "Output.txt") or Excel file with file extension ".xlsx" (e.g., "Output.xlsx"). If the file name does not contain any file extension, an Excel file will be written.
append	logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.
check	logical: if TRUE (default), argument specification is checked.
output	logical: if TRUE (default), output is shown.

Value

Returns an object of class misty.object, which is a list with following entries:

call	function call
type	type of analysis
x	a fitted model of class "lavaan"
args	specification of function arguments
model	specified models, i.e., mod.l1 for the model at the Within level, mod.l1.syntax for the lavaan syntax for the model at the Between level, mod.l2 for the model at the Within level, mod.l2.syntax for the lavaan syntax for the model at the Between level, mod.l12 for the model at the Within and Between level, mod.l12.syntax for the lavaan syntax for the model at the Within and Between level, l1.mod.base for the baseline model at the Within level saturated at the Between level, l1.mod.hypo for the hypothesized model at the Within level saturated at the Between level, l2.mod.base for the baseline model at the Between level saturated at the Within level, l2.mod.hypo for the hypothesized model at the Between level saturated at the Within level
result	list with result tables, i.e., summary for the summary of the specification of the estimation method and missing data handling in lavaan and fit for the model fit information.

Note

The function uses the functions cfa, fitmeasures, lavInspect, lavTech, and parTable provided in the R package lavaan by Yves Rosseel (2012).

Author(s)

Takuya Yanagida [e-mail]

References

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48, 1-36. https://doi.org/10.18637/jss.v048.i02

See Also

multilevel.cfa, multilevel.invar, multilevel.omega, multilevel.cor, multilevel.descript

Examples

## Not run: 
# Load data set "Demo.twolevel" in the lavaan package
data("Demo.twolevel", package = "lavaan")

# Model specification
model <- 'level: 1
              fw =~ y1 + y2 + y3
              fw ~ x1 + x2 + x3
           level: 2
              fb =~ y1 + y2 + y3
              fb ~ w1 + w2'

#-------------------------------------------------------------------------------

# Example 1: Model estimation with estimator = "ML"
fit1 <- lavaan::sem(model = model, data = Demo.twolevel, cluster = "cluster",
                    estimator = "ML")

# Simultaneous and level-specific multilevel model fit information
ls.fit1 <- multilevel.fit(fit1)

# Write results into a text file
multilevel.fit(fit1, write = "LS-Fit1.txt")

# Write results into an Excel file
write.result(ls.fit1, "LS-Fit1.xlsx")

# Example 2: Model estimation with estimator = "MLR"
fit2 <- lavaan::sem(model = model, data = Demo.twolevel, cluster = "cluster",
                    estimator = "MLR")

# Simultaneous and level-specific multilevel model fit information
# Write results into an Excel file
multilevel.fit(fit2, write = "LS-Fit2.xlsx")

## End(Not run)

misty documentation built on Oct. 24, 2024, 5:10 p.m.